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nen.py
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nen.py
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import nengo
import nengo.spa as spa
from nengo.spa import Vocabulary
import numpy as np
dim = 64
rng = np.random.RandomState(0)
vocab = Vocabulary(dimensions=dim, rng=rng)
mysent = "Boys chase dogs.".upper()
def custom_parser(sent):
# Assume S V O
S, V, O = sent[:-1].split()
return (S, V, O)
for w in custom_parser(mysent):
w_up = w.upper()
w_sp = vocab.parse(w_up.upper())
exec("{} = w_sp".format(w_up))
# BOY = vocab.parse('BOY')
# DOG = vocab.parse('DOG')
# CHASE = vocab.parse('CHASE')
# HUG = vocab.parse('HUG')
AGENT = vocab.parse('AGENT')
VERB = vocab.parse('VERB')
THEME = vocab.parse('THEME')
def conv_expression(parsed):
S, V, O = parsed
return "p = VERB * {} + AGENT * {} + THEME * {}".format(V, S, O)
model = spa.SPA(label=mysent, vocabs=[vocab])
with model:
model.p = spa.State(dimensions=dim, label='p')
# model.t = spa.State(dimensions=dim, label='t')
# model.z = spa.State(dimensions=dim, label='z')
model.out_agent = spa.State(dimensions=dim, label='out_agent')
model.out_verb = spa.State(dimensions=dim, label='out_verb')
model.out_theme = spa.State(dimensions=dim, label='out_theme')
actions = spa.Actions(
# 'p = VERB * CHASE + AGENT * DOG + THEME * BOY',
conv_expression(custom_parser(mysent)),
'out_agent = p * ~AGENT',
'out_verb = p * ~VERB',
'out_theme = p * ~THEME',
)
model.cortical = spa.Cortical(actions)